If the job description asks for a minimum of 5 years of experience, it needs to include an explanation of why 4 years isn’t enough.
2/11 #DataScience#MachineLearning#Hiring
After 2 rounds of interviews, the company needs to explain what additional information they expect to get from this round and why they didn’t get it during the last round.
3/11 #DataScience#MachineLearning#Hiring
The hiring manager must spend as much time giving feedback to each rejected candidate as it takes for a candidate to apply for the job.
4/11 #DataScience#MachineLearning#Hiring
None of these are realistic, but neither is the hiring process. The only way for me to make the strangeness of the whole thing obvious is through hyperbole.
5/11 #DataScience#MachineLearning#Hiring
If you’re a senior leader reading this and thinking, ‘That’s absurd!’ Now you know how candidates feel looking at the hiring process.
6/11 #DataScience#MachineLearning#Hiring
It’s ineffective, and we’re all indoctrinated into thinking it isn’t. It’s a Rolls Royce concept car that can’t get past a speed bump because of the wheels’ design.
7/11 #DataScience#MachineLearning#Hiring
I'm passionate about fixing the hiring process because companies will go out of business if they don’t get access to data and analytics talent. I could design a dominant strategy for a client, but empty chairs can’t execute.
8/11 #DataScience#MachineLearning#Hiring
I started seeing the impacts on execution five years ago, and it’s gotten worse since then. Transforming the hiring process must be part of the digital, data, and intelligent transformation plans.
9/11 #DataScience#MachineLearning#Hiring
It needs a C-Suite sponsor, and leadership goals should include hiring process continuous improvement KPIs.
Hiring needs strategic leadership and planning for businesses to attract the talent required to execute successfully.
10/11 #DataScience#MachineLearning#Hiring
Is it time to quickly improve your hiring process and get access to top talent? I help clients improve offer acceptance rates, selection ratios, time to hire, and hire quality.
Data Scientist Job Openings On LinkedIn:
March - 138K
Now - 134K
Hiring is slowing for mid to junior-level roles. That's the first sign of tightening budgets and more changes will come quickly. Let me explain what comes next.
1/14 #DataScience#MachineLearning#Leadership
Higher costs are compressing margins for businesses across industries. Revenue growth has stagnated. Both factors mean businesses must find ways to cut costs or they are in danger.
2/14 #DataScience#MachineLearning#Leadership
Missing on revenue projections or lowering guidance for the rest of the year is a death sentence for share prices. The C Suite is measured by share price so they're moving quickly to cut costs.
3/14 #DataScience#MachineLearning#Leadership
Data Scientists looking for a new role and Recruiters looking for candidates speak 2 different languages. Miscommunication is the most common reason candidates disengage, drop out of the interview process, and reject offers. Why?
1/12 #DataScience#Recruiting#Hiring
Candidates eventually find out the role isn’t what they expected and there's not way to keep them involved in the process after that.
2/12 #DataScience#Recruiting#Hiring
Explaining a role to a Machine Learning Engineer vs. Data Engineer vs. Applied Researcher vs. Generalist Data Scientist vs. Data Analyst are all different conversations.
3/12 #DataScience#Recruiting#Hiring
Supervised deep learning is limited by label quality. An ontology must be built before labeling begins. That's a graph defining concepts and their connections. Ontologies guide labeling to ensure consistency and completeness. 1/5 #DataScience#MachineLearning#DeepLearning
Any problem space including people introduces multiple, often conflicting ontologies. Datasets ideally have multiple labels and require multiple models to be trained. 2/5 #DataScience#MachineLearning#DeepLearning
Most projects have a single, majority consensus labeling methodology. Where ontologies diverge from or conflict with it, inference will be inaccurate no matter how incredible the models we use become. 3/5 #DataScience#MachineLearning#DeepLearning
Approach your Data Science learning path strategically. Start by asking, ‘why do people build models?’ I'm going to explain a more effective approach to learning our field that focuses on applications over theory.
1/10 #DataScience#MachineLearning#CareerAdvice
Most use cases in the business world don’t use complex machine learning or deep learning. It’s mostly analytics and simple models.
Why do people build datasets? Datasets introduce new knowledge into the business. Having data is not enough. The dataset must contain new knowledge.
3/10 #DataScience#MachineLearning#CareerAdvice
Open-sourcing Twitter’s algorithm isn’t what most people think it is. I don’t think even Elon Musk or most people at Twitter really understand where this process goes.
1/10 #DataScience#MachineLearning#Twitter
The code is not very insightful. The model itself is too complex for people to understand and interact with. So, what does open-sourcing the algorithm look like?
2/10 #DataScience#MachineLearning#Twitter
It’s the ability to click on a Tweet in your timeline and get a detailed explanation of why it was served to you. There are levels of model explainability.
3/10 #DataScience#MachineLearning#Twitter
How will companies move into the Metaverse? Most platform-based businesses are already there. Google, Amazon, and Facebook are all platform native companies so they have a clear lane into the Metaverse. 1/7 #Metaverse#Strategy
Their businesses have always been digital-first and built on a platform with access to a business ecosystem or marketplace. Building an increasingly capable platform grew their accessible ecosystems. 2/7 #Metaverse#Strategy
Platforms remove barriers to scale so a company like Amazon could disrupt and rapidly take market share from retail incumbents. Google and Facebook entered emerging, very small ecosystems-Google for search and Facebook for social. 3/7 #Metaverse#Strategy